{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/1.0-Variational-Autoencoder-fashion-mnist.ipynb)\n",
    "## Variational Autoencoder (VAE) ([article](https://arxiv.org/abs/1312.6114)) \n",
    "\n",
    "The original variational autoencoder network,  using [tensorflow_probability](https://github.com/tensorflow/probability)\n",
    "\n",
    "![variational autoencoder](imgs/vae.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install packages if in colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### install necessary packages if in colab\n",
    "def run_subprocess_command(cmd):\n",
    "    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)\n",
    "    for line in process.stdout:\n",
    "        print(line.decode().strip())\n",
    "\n",
    "\n",
    "import sys, subprocess\n",
    "\n",
    "IN_COLAB = \"google.colab\" in sys.modules\n",
    "colab_requirements = [\n",
    "    \"pip install tf-nightly-gpu-2.0-preview==2.0.0.dev20190513\",\n",
    "    \"pip install tfp-nightly==0.7.0.dev20190508\",\n",
    "]\n",
    "if IN_COLAB:\n",
    "    for i in colab_requirements:\n",
    "        run_subprocess_command(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:29.886938Z",
     "start_time": "2019-05-10T19:17:26.656978Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/cube/tsainbur/conda_envs/tpy3/lib/python3.6/site-packages/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.autonotebook import tqdm\n",
    "%matplotlib inline\n",
    "from IPython import display\n",
    "import pandas as pd\n",
    "\n",
    "# the nightly build of tensorflow_probability is required as of the time of writing this \n",
    "import tensorflow_probability as tfp\n",
    "ds = tfp.distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:29.891899Z",
     "start_time": "2019-05-10T19:17:29.888777Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-alpha0 0.7.0-dev20190508\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__, tfp.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a fashion-MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:29.985874Z",
     "start_time": "2019-05-10T19:17:29.893678Z"
    }
   },
   "outputs": [],
   "source": [
    "TRAIN_BUF=60000\n",
    "BATCH_SIZE=512\n",
    "TEST_BUF=10000\n",
    "DIMS = (28,28,1)\n",
    "N_TRAIN_BATCHES =int(TRAIN_BUF/BATCH_SIZE)\n",
    "N_TEST_BATCHES = int(TEST_BUF/BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:35.609703Z",
     "start_time": "2019-05-10T19:17:29.987583Z"
    }
   },
   "outputs": [],
   "source": [
    "# load dataset\n",
    "(train_images, _), (test_images, _) = tf.keras.datasets.fashion_mnist.load_data()\n",
    "\n",
    "# split dataset\n",
    "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype(\n",
    "    \"float32\"\n",
    ") / 255.0\n",
    "test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype(\"float32\") / 255.0\n",
    "\n",
    "# batch datasets\n",
    "train_dataset = (\n",
    "    tf.data.Dataset.from_tensor_slices(train_images)\n",
    "    .shuffle(TRAIN_BUF)\n",
    "    .batch(BATCH_SIZE)\n",
    ")\n",
    "test_dataset = (\n",
    "    tf.data.Dataset.from_tensor_slices(test_images)\n",
    "    .shuffle(TEST_BUF)\n",
    "    .batch(BATCH_SIZE)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the network as tf.keras.model object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:35.629037Z",
     "start_time": "2019-05-10T19:17:35.611888Z"
    }
   },
   "outputs": [],
   "source": [
    "class VAE(tf.keras.Model):\n",
    "    \"\"\"a basic vae class for tensorflow\n",
    "    Extends:\n",
    "        tf.keras.Model\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        super(VAE, self).__init__()\n",
    "        self.__dict__.update(kwargs)\n",
    "\n",
    "        self.enc = tf.keras.Sequential(self.enc)\n",
    "        self.dec = tf.keras.Sequential(self.dec)\n",
    "\n",
    "    def encode(self, x):\n",
    "        mu, sigma = tf.split(self.enc(x), num_or_size_splits=2, axis=1)\n",
    "        return ds.MultivariateNormalDiag(loc=mu, scale_diag=sigma)\n",
    "\n",
    "    def reparameterize(self, mean, logvar):\n",
    "        eps = tf.random.normal(shape=mean.shape)\n",
    "        return eps * tf.exp(logvar * 0.5) + mean\n",
    "\n",
    "    def reconstruct(self, x):\n",
    "        mu, _ = tf.split(self.enc(x), num_or_size_splits=2, axis=1)\n",
    "        return self.decode(mu)\n",
    "\n",
    "    def decode(self, z):\n",
    "        return self.dec(z)\n",
    "\n",
    "    def compute_loss(self, x):\n",
    "\n",
    "        q_z = self.encode(x)\n",
    "        z = q_z.sample()\n",
    "        x_recon = self.decode(z)\n",
    "        p_z = ds.MultivariateNormalDiag(\n",
    "          loc=[0.] * z.shape[-1], scale_diag=[1.] * z.shape[-1]\n",
    "          )\n",
    "        kl_div = ds.kl_divergence(q_z, p_z)\n",
    "        latent_loss = tf.reduce_mean(tf.maximum(kl_div, 0))\n",
    "        recon_loss = tf.reduce_mean(tf.reduce_sum(tf.math.square(x - x_recon), axis=0))\n",
    "\n",
    "        return recon_loss, latent_loss\n",
    "\n",
    "    def compute_gradients(self, x):\n",
    "        with tf.GradientTape() as tape:\n",
    "            loss = self.compute_loss(x)\n",
    "        return tape.gradient(loss, self.trainable_variables)\n",
    "\n",
    "    @tf.function\n",
    "    def train(self, train_x):\n",
    "        gradients = self.compute_gradients(train_x)\n",
    "        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the network architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:35.869467Z",
     "start_time": "2019-05-10T19:17:35.630938Z"
    }
   },
   "outputs": [],
   "source": [
    "N_Z = 2\n",
    "encoder = [\n",
    "    tf.keras.layers.InputLayer(input_shape=DIMS),\n",
    "    tf.keras.layers.Conv2D(\n",
    "        filters=32, kernel_size=3, strides=(2, 2), activation=\"relu\"\n",
    "    ),\n",
    "    tf.keras.layers.Conv2D(\n",
    "        filters=64, kernel_size=3, strides=(2, 2), activation=\"relu\"\n",
    "    ),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(units=N_Z*2),\n",
    "]\n",
    "\n",
    "decoder = [\n",
    "    tf.keras.layers.Dense(units=7 * 7 * 64, activation=\"relu\"),\n",
    "    tf.keras.layers.Reshape(target_shape=(7, 7, 64)),\n",
    "    tf.keras.layers.Conv2DTranspose(\n",
    "        filters=64, kernel_size=3, strides=(2, 2), padding=\"SAME\", activation=\"relu\"\n",
    "    ),\n",
    "    tf.keras.layers.Conv2DTranspose(\n",
    "        filters=32, kernel_size=3, strides=(2, 2), padding=\"SAME\", activation=\"relu\"\n",
    "    ),\n",
    "    tf.keras.layers.Conv2DTranspose(\n",
    "        filters=1, kernel_size=3, strides=(1, 1), padding=\"SAME\", activation=\"sigmoid\"\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T18:40:40.306731Z",
     "start_time": "2019-05-10T18:40:40.292930Z"
    }
   },
   "source": [
    "### Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:36.796282Z",
     "start_time": "2019-05-10T19:17:35.872282Z"
    }
   },
   "outputs": [],
   "source": [
    "# the optimizer for the model\n",
    "optimizer = tf.keras.optimizers.Adam(1e-3)\n",
    "# train the model\n",
    "model = VAE(\n",
    "    enc = encoder,\n",
    "    dec = decoder,\n",
    "    optimizer = optimizer,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:37.443010Z",
     "start_time": "2019-05-10T19:17:36.800302Z"
    }
   },
   "outputs": [],
   "source": [
    "# exampled data for plotting results\n",
    "example_data = next(iter(test_dataset))\n",
    "\n",
    "\n",
    "def plot_reconstruction(model, example_data, nex=8, zm=2):\n",
    "\n",
    "    example_data_reconstructed = model.reconstruct(example_data)\n",
    "    samples = model.decode(tf.random.normal(shape=(BATCH_SIZE, N_Z)))\n",
    "    fig, axs = plt.subplots(ncols=nex, nrows=3, figsize=(zm * nex, zm * 3))\n",
    "    for axi, (dat, lab) in enumerate(\n",
    "        zip(\n",
    "            [example_data, example_data_reconstructed, samples],\n",
    "            [\"data\", \"data recon\", \"samples\"],\n",
    "        )\n",
    "    ):\n",
    "        for ex in range(nex):\n",
    "            axs[axi, ex].matshow(\n",
    "                dat.numpy()[ex].squeeze(), cmap=plt.cm.Greys, vmin=0, vmax=1\n",
    "            )\n",
    "            axs[axi, ex].axes.get_xaxis().set_ticks([])\n",
    "            axs[axi, ex].axes.get_yaxis().set_ticks([])\n",
    "        axs[axi, 0].set_ylabel(lab)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:17:37.456384Z",
     "start_time": "2019-05-10T19:17:37.445204Z"
    }
   },
   "outputs": [],
   "source": [
    "# a pandas dataframe to save the loss information to\n",
    "losses = pd.DataFrame(columns = ['recon_loss', 'latent_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:23:40.342635Z",
     "start_time": "2019-05-10T19:17:37.458290Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 49 | recon_loss: 15.782215118408203 | latent_loss: 4.198213577270508\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_epochs = 50\n",
    "for epoch in range(n_epochs):\n",
    "    # train\n",
    "    for batch, train_x in tqdm(\n",
    "        zip(range(N_TRAIN_BATCHES), train_dataset), total=N_TRAIN_BATCHES\n",
    "    ):\n",
    "        model.train(train_x)\n",
    "    # test on holdout\n",
    "    loss = []\n",
    "    for batch, test_x in tqdm(\n",
    "        zip(range(N_TEST_BATCHES), test_dataset), total=N_TEST_BATCHES\n",
    "    ):\n",
    "        loss.append(model.compute_loss(train_x))\n",
    "    losses.loc[len(losses)] = np.mean(loss, axis=0)\n",
    "    # plot results\n",
    "    display.clear_output()\n",
    "    print(\n",
    "        \"Epoch: {} | recon_loss: {} | latent_loss: {}\".format(\n",
    "            epoch, losses.recon_loss.values[-1], losses.latent_loss.values[-1]\n",
    "        )\n",
    "    )\n",
    "    plot_reconstruction(model, example_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show grid in 2D latent space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-10T19:28:20.049203Z",
     "start_time": "2019-05-10T19:28:19.787382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 279.5, 279.5, -0.5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample from grid\n",
    "nx = ny =10\n",
    "meshgrid = np.meshgrid(np.linspace(-3, 3, nx), np.linspace(-3, 3, ny))\n",
    "meshgrid = np.array(meshgrid).reshape(2, nx*ny).T\n",
    "x_grid = model.decode(meshgrid)\n",
    "x_grid = x_grid.numpy().reshape(nx, ny, 28,28, 1)\n",
    "# fill canvas\n",
    "canvas = np.zeros((nx*28, ny*28))\n",
    "for xi in range(nx):\n",
    "    for yi in range(ny):\n",
    "        canvas[xi*28:xi*28+28, yi*28:yi*28+28] = x_grid[xi, yi,:,:,:].squeeze()\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.matshow(canvas, cmap=plt.cm.Greys)\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
